ArticleBriefings in bioinformatics2025
Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Caution: fundamental data quality issues underlying intelligent prediction of enzyme optimal pH.Briefings in bioinformatics · 2026Article
- Response to "Caution: Fundamental data quality issues underlying intelligent prediction of enzyme optimal pH".Briefings in bioinformatics · 2026Article
- Predicting Enzyme pH Optima from Structure Using Equivariant Graph Neural Networks.Journal of chemical information and modeling · 2026Article
- Response to "Addressing flaws in the Seq2Topt dataset for the prediction of enzyme optimal temperature".Briefings in bioinformatics · 2026Article
- Addressing flaws in the Seq2Topt dataset for the prediction of enzyme optimal temperature.Briefings in bioinformatics · 2026Article
- Machine learning for enzyme catalytic activity: current progress and future horizons.Briefings in bioinformatics · 2026Review
- GotEnzymes2: expanding coverage of enzyme kinetics and thermal properties.Nucleic acids research · 2026Article
- EnzymeSifter: a tool for discovery of industrial enzymes from metagenomes.Bioinformatics advances · 2026Article
- NanoBind: Mechanism-Driven Deep Learning of Nanobody-Antigen Molecular Recognition.Research (Washington, D.C.) · 2026Article
- Deep Learning-Based Prediction of Enzyme Optimal pH and Design of Point Mutations to Improve Acid Resistance.ACS synthetic biology · 2025Article
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5 authors.
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Abstract
An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study, Seq2Topt, reached a superior accuracy on ${T}_{opt}$ prediction just using protein sequences (RMSE = 12.26°C and R2 = 0.57), and could capture key protein regions for enzyme ${T}_{opt}$ with multi-head attention on residues. Through case studies on thermophilic enzyme selection and predicting enzyme ${T}_{opt}$ shifts caused by point mutations, Seq2Topt was demonstrated as a promising computational tool for enzyme mining and in-silico enzyme design. Additionally, accurate deep learning predictors of enzyme optimal pH (Seq2pHopt, RMSE = 0.88 and R2 = 0.42) and melting temperature (Seq2Tm, RMSE = 7.57 °C and R2 = 0.64) were developed based on the model architecture of Seq2Topt, suggesting that the development of Seq2Topt could potentially give rise to a useful prediction platform of enzymes.
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